基于机器学习的汽车企业研发投入强度与财务绩效的影响研究

Research on the Impact of R&D Investment Intensity and Financial Performance of Automobile Enterprises Based on Machine Learning

摘要: 在全球碳中和与智能化浪潮驱动汽车产业深度转型的背景下，研发投入已成为车企构建核心竞争力的关键。本文创新性地采用XGBoost机器学习模型，突破传统研究方法的局限，深入剖析了汽车企业研发投入强度对财务绩效的非线性影响。基于156家A股上市汽车公司2021年的数据分析，并对XGBoost模型参数优化。研究发现研发人员数量、研发人员占比及研发投入营收比是驱动财务绩效的关键要素，且研发团队规模与企业财务表现呈显著正相关。据此，本文为汽车企业提出切实可行的建议，包括加强研发团队建设、优化研发投入规模和提升研发资金效率。本研究不仅为汽车企业战略决策提供理论支撑，也为政策制定者和投资者理解研发投入的价值、把握汽车产业发展趋势提供重要参考。

Abstract: Against the backdrop of the global carbon neutrality and intelligentization wave driving a profound transformation in the automotive industry, research and development (R&D) investment has become a crucial strategic element for automotive enterprises to build core competitiveness. This paper innovatively employs the XGBoost machine learning model, breaking through the limitations of traditional research methods, to deeply analyze the non-linear impact of R&D investment intensity on the financial performance of automotive companies. Based on the data analysis of 156 A-share listed automotive companies in 2021, and through the optimization of XGBoost model parameters, the study finds that the number of R&D personnel, the proportion of R&D personnel, and the ratio of R&D expenditure to operating revenue are key factors driving financial performance, and that there is a significant positive correlation between the size of the R&D team and the company’s financial performance. Accordingly, this paper proposes practical and feasible recommendations for automotive companies, including strengthening R&D team construction, optimizing the scale of R&D investment, and improving the efficiency of R&D fund utilization. This research not only provides theoretical support for strategic decision-making in automotive enterprises but also offers an important reference for policymakers and investors to understand the value of R&D investment and grasp the development trends of the automotive industry.